The interest in fractional-order (FO) control can be traced back to the late nineteenth century. The growing tendency towards using fractional-order proportional-integral-derivative (FOPID) control has been fueled mainly by the fact that these controllers have additional “tuning knobs” that allow coherent adjustment of the dynamics of control systems. For instance, in certain cases, the capacity for additional frequency response shaping gives rise to the generation of control laws that lead to superior performance of control loops. These fractional-order control laws may allow fulfilling intricate control performance requirements that are otherwise not in the span of conventional integer-order control systems. However, there are underpinning points that are rarely addressed in the literature: (1) What are the particular advantages (in concrete figures) of FOPID controllers versus conventional, integer-order (IO) PID controllers in light of the complexities arising in the implementation of the former? (2) For real-time implementation of FOPID controllers, approximations are used that are indeed equivalent to high-order linear controllers. What, then, is the benefit of using FOPID controllers? Finally, (3) What advantages are to be had from having a near-ideal fractional-order behavior in control practice? In the present paper, we attempt to address these issues by reviewing a large portion of relevant publications in the fast-growing FO control literature, outline the milestones and drawbacks, and present future perspectives for industrialization of fractional-order control. Furthermore, we comment on FOPID controller tuning methods from the perspective of seeking globally optimal tuning parameter sets and how this approach can benefit designers of industrial FOPID control. We also review some CACSD (computer-aided control system design) software toolboxes used for the design and implementation of FOPID controllers. Finally, we draw conclusions and formulate suggestions for future research.

Practical work is one of the most important instructional tools in control engineering. To address concerns linked to the cost and space requirements of traditional hands-on laboratories, technology-enabled laboratory modes, such as virtual, remote, and take-home laboratory modes are proposed. Each of these alliterative laboratory modes has its own set of benefits and emphasizes a distinct learning goal. Furthermore, due to lockdown and physical proximity restrictions imposed by policies in response to the COVID-19 pandemic, the employment of these laboratory modes has been quickly increasing. The laboratories’ development, operation, and maintenance become more fragmented as a result of these many possibilities. In this study, we propose “ ReImagine Lab” as a framework for leveraging digital twins and extended reality technologies to streamline the development and operation of hands-on, virtual, and remote laboratories. By increasing the level of interaction, immersion, and collaboration in technology-enabled laboratory forms, this framework intends to boost student engagement. The benefits of this framework are demonstrated by examining several use cases, and a 37-person “system usability study” is conducted to assess the usability of virtual laboratories employing desktop computers and immersive virtual reality.

Reduction in system inertia and maintaining the frequency at the nominal value is a staple of today's and future power systems since their operation, stability, and resiliency are degraded by frequency oscillation and cascading failures. Consequently, designing a stable, scalable, and robust virtual inertia control system is highly relevant to skillfully diminishing the deviations during major contingencies. Therefore, considering the potential problems in predesigned nonflexible control systems with offline tuning techniques, we propose a variable fractional-order PID controller for virtual inertia control applications, which is tuned online using a modified neural network-based algorithm. The new proposed tuner algorithm is trained using a deep reinforcement learning strategy with a simplified deep deterministic policy gradient, which considers microgrid uncertainties. Compared with existing methods, all the tuning knobs of the discrete type and fully tunable variable FOPID controller (for both gain and order) can be captured based on the proposed hybrid algorithm, which inherits features from both classical and advanced techniques. To demonstrate the effectiveness of the training of the proposed controller, a comparative analysis with the standard FOPID and PID controllers is given under three different scenarios with a smooth (dis)connection of renewable energy sources and loads.